Heterogeneous Causal Effects on Well-being and Cooperation in New Zealand
Three Questions:
Flourishing – does attendance enhance multi‑dimensional well‑being (“the good life”)?
Cooperation – does attendance spur pro-social behaviour?
Individual Differences - who responds differently?
On Average The Walk Is Flat
Note
Technical details in Supplements
| Stage | Tool | Purpose |
|---|---|---|
| 1. Target | Causal estimand definition | Binary & shift interventions |
| 2. Learn | SuperLearner (semi-parametric ML) | Outcome & censoring models |
| 3. Adjust | IPC weights + census post-stratification | Population inference |
| 4. Validate | 10-fold cross-validation | Out-of-sample performance |
| 5. Subgroup | Causal forests & policy trees | Detect heterogeneity |
Note
Technical details in Supplements
| Estimand | Description | Example |
|---|---|---|
| Binary | All or nothing | 0 vs 1 service/month |
| Shift-up | Increase unless weekly | 0 → 1, 1 → 2, 2 → 3, 3 → 4 |
| Shift-down | Decrease to zero | 4 → 0, 3 → 0, 2 → 0, 1 → 0 |
Note
Technical details in Supplements
Figure 1: ATE: Three-Wave: Binary Intervention
Figure 2: ATE: Three-wave Shift Intervention
Figure 3: ATE: Three-wave Soft Intervention
Figure 4: ATE: Six-wave Soft Intervention GAIN: +1
Figure 5: ATE: Six-wave Soft Intervention LOSS: -1
Figure 6: CATE RATE
Figure 8: ATE: Three-Wave: Binary Intervention
Figure 9: ATE: Three-wave Shift Intervention
Figure 10: ATE: Three-wave Soft Intervention
Figure 11: ATE: Six-wave Soft Intervention WEEKLY/ZER0
Figure 12: ATE: Six-wave Soft Intervention LOSS: -1
Figure 14: Policy Trsees: Forgiveness
Figure 15: Policy Trees: Gratitude
Figure 16: Policy Trees for Hlth Fatigue Outcome
Figure 17: Policy Trees: Meaning Sense
Figure 18: Policy Trees: Neighbourhood Community
Figure 19: Policy Trees: Self Esteem